COMPMID-1806: Add VGG-base VDSR network

Change-Id: Ifd7a0b7afa10ad1fcc123aa7c1c29460c4e447d7
Reviewed-on: https://review.mlplatform.org/348
Reviewed-by: Anthony Barbier <Anthony.barbier@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 181 insertions, 0 deletions

diff --git a/examples/graph_vgg_vdsr.cpp b/examples/graph_vgg_vdsr.cpp
new file mode 100644
index 0000000000..63fa604584
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+++ b/examples/graph_vgg_vdsr.cpp
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+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/graph.h"
+#include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
+#include "utils/GraphUtils.h"
+#include "utils/Utils.h"
+
+using namespace arm_compute;
+using namespace arm_compute::utils;
+using namespace arm_compute::graph::frontend;
+using namespace arm_compute::graph_utils;
+
+/** Example demonstrating how to implement VGG based VDSR network using the Compute Library's graph API */
+class GraphVDSRExample : public Example
+{
+public:
+    GraphVDSRExample()
+        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "VDSR")
+    {
+        model_input_width  = cmd_parser.add_option<SimpleOption<unsigned int>>("image-width", 192);
+        model_input_height = cmd_parser.add_option<SimpleOption<unsigned int>>("image-height", 192);
+
+        // Add model id option
+        model_input_width->set_help("Input image width.");
+        model_input_height->set_help("Input image height.");
+    }
+    GraphVDSRExample(const GraphVDSRExample &) = delete;
+    GraphVDSRExample &operator=(const GraphVDSRExample &) = delete;
+    GraphVDSRExample(GraphVDSRExample &&)                 = default; // NOLINT
+    GraphVDSRExample &operator=(GraphVDSRExample &&) = default;      // NOLINT
+    ~GraphVDSRExample() override                     = default;
+    bool do_setup(int argc, char **argv) override
+    {
+        // Parse arguments
+        cmd_parser.parse(argc, argv);
+
+        // Consume common parameters
+        common_params = consume_common_graph_parameters(common_opts);
+
+        // Return when help menu is requested
+        if(common_params.help)
+        {
+            cmd_parser.print_help(argv[0]);
+            return false;
+        }
+
+        // Get input image width and height
+        const unsigned int image_width  = model_input_width->value();
+        const unsigned int image_height = model_input_height->value();
+
+        // Print parameter values
+        std::cout << common_params << std::endl;
+        std::cout << "Image width: " << image_width << std::endl;
+        std::cout << "Image height: " << image_height << std::endl;
+
+        ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type) && common_params.target == Target::NEON,
+                                "QASYMM8 not supported for this graph");
+
+        // Get trainable parameters data path
+        const std::string data_path  = common_params.data_path;
+        const std::string model_path = "/cnn_data/vdsr_model/";
+
+        // Create a preprocessor object
+        std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
+
+        // Create input descriptor
+        const TensorShape tensor_shape     = permute_shape(TensorShape(image_width, image_height, 1U, 1U), DataLayout::NCHW, common_params.data_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+
+        // Set weights trained layout
+        const DataLayout weights_layout = DataLayout::NCHW;
+
+        // Note: Quantization info are random and used only for benchmarking purposes
+        graph << common_params.target
+              << common_params.fast_math_hint
+              << InputLayer(input_descriptor.set_quantization_info(QuantizationInfo(0.0078125f, 128)),
+                            get_input_accessor(common_params, std::move(preprocessor), false));
+
+        SubStream left(graph);
+        SubStream right(graph);
+
+        // Layer 1
+        right << ConvolutionLayer(
+                  3U, 3U, 64U,
+                  get_weights_accessor(data_path, "conv0_w.npy", weights_layout),
+                  get_weights_accessor(data_path, "conv0_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1), 1, QuantizationInfo(0.031778190285f, 156), QuantizationInfo(0.0784313753247f, 128))
+              .set_name("conv0")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu");
+
+        // Rest 17 layers
+        for(unsigned int i = 1; i < 19; ++i)
+        {
+            const std::string conv_w_path = "conv" + arm_compute::support::cpp11::to_string(i) + "_w.npy";
+            const std::string conv_b_path = "conv" + arm_compute::support::cpp11::to_string(i) + "_b.npy";
+            const std::string conv_name   = "conv" + arm_compute::support::cpp11::to_string(i);
+            right << ConvolutionLayer(
+                      3U, 3U, 64U,
+                      get_weights_accessor(data_path, conv_w_path, weights_layout),
+                      get_weights_accessor(data_path, conv_b_path),
+                      PadStrideInfo(1, 1, 1, 1), 1, QuantizationInfo(0.015851572156f, 93))
+                  .set_name(conv_name)
+                  << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(conv_name + "/Relu");
+        }
+
+        // Final layer
+        right << ConvolutionLayer(
+                  3U, 3U, 1U,
+                  get_weights_accessor(data_path, "conv20_w.npy", weights_layout),
+                  get_weights_accessor(data_path, "conv20_b.npy"),
+                  PadStrideInfo(1, 1, 1, 1), 1, QuantizationInfo(0.015851572156f, 93))
+              .set_name("conv20")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv20/Relu");
+
+        // Add residual to input
+        graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name("add")
+              << OutputLayer(arm_compute::support::cpp14::make_unique<DummyAccessor>(0));
+
+        // Finalize graph
+        GraphConfig config;
+        config.num_threads = common_params.threads;
+        config.use_tuner   = common_params.enable_tuner;
+        config.tuner_file  = common_params.tuner_file;
+
+        graph.finalize(common_params.target, config);
+
+        return true;
+    }
+    void do_run() override
+    {
+        // Run graph
+        graph.run();
+    }
+
+private:
+    CommandLineParser           cmd_parser;
+    CommonGraphOptions          common_opts;
+    SimpleOption<unsigned int> *model_input_width{ nullptr };
+    SimpleOption<unsigned int> *model_input_height{ nullptr };
+    CommonGraphParams           common_params;
+    Stream                      graph;
+};
+
+/** Main program for VGG-based VDSR
+ *
+ * Model is based on:
+ *      https://arxiv.org/pdf/1511.04587.pdf
+ *      "Accurate Image Super-Resolution Using Very Deep Convolutional Networks"
+ *      Jiwon Kim, Jung Kwon Lee and Kyoung Mu Lee
+ *
+ * @note To list all the possible arguments execute the binary appended with the --help option
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments
+ */
+int main(int argc, char **argv)
+{
+    return arm_compute::utils::run_example<GraphVDSRExample>(argc, argv);
+}